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"The three parts of the book give an excellent overview of current developments and the status of a still developing and emerging field. In this way, it helps researchers in psychometrics and educational assessment to familiarize themselves with Bayesian networks, and their applications. ... The targeted audience of the book seems to focus on students at the graduate level. In this way, it is written for a large audience, and it suits the needs of many researchers." (Nikky van Buuren, Sebastiaan de Klerk and Bernard P. Veldkamp, Psychometrika, Vol. 82, 2017) "This book will provide valuable information on using data-mining along with graphical models in educational assessment. It is one of the initial works that well explain the operative procedures of designing, validating, and implementing the data-driven, competency-oriented diagnostic assessment. The book should be a good reference for both scholars and practitioners in the areas of educational assessment, learning environments and curriculum design, and school improvement." (Fengfeng Ke, Technology, Knowledge and Learning, Vol. 24, 2019)
Introduction.- An Introduction to Evidence-Centered Design.- Bayesian Probability and Statistics: a review.- Basic graph theory and graphical models.- Efficient calculations.- Some Example Networks.- Explanation and Test Construction.- Parameters for Bayesian Network Models.- Learning in Models with Fixed Structure.- Critiquing and Learning Model Structure.- An Illustrative Example.- The Conceptual Assessment Framework.- The Evidence Accumulation Process.- The Biomass Measurement Model.- The Future of Bayesian Networks in Educational Assessment.- Bayesian Network Resources.- References.